Overview

Dataset statistics

Number of variables12
Number of observations1461
Missing cells25
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.1 KiB
Average record size in memory96.1 B

Variable types

DateTime1
Numeric11

Alerts

PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] is highly correlated with tsunHigh correlation
tavg is highly correlated with tmin and 2 other fieldsHigh correlation
tmin is highly correlated with tavg and 1 other fieldsHigh correlation
tmax is highly correlated with tavg and 2 other fieldsHigh correlation
wspd is highly correlated with wpgtHigh correlation
wpgt is highly correlated with wspdHigh correlation
tsun is highly correlated with PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] and 2 other fieldsHigh correlation
PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] is highly correlated with tsunHigh correlation
tavg is highly correlated with tmin and 2 other fieldsHigh correlation
tmin is highly correlated with tavg and 1 other fieldsHigh correlation
tmax is highly correlated with tavg and 2 other fieldsHigh correlation
wspd is highly correlated with wpgtHigh correlation
wpgt is highly correlated with wspdHigh correlation
tsun is highly correlated with PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] and 2 other fieldsHigh correlation
tavg is highly correlated with tmin and 1 other fieldsHigh correlation
tmin is highly correlated with tavg and 1 other fieldsHigh correlation
tmax is highly correlated with tavg and 1 other fieldsHigh correlation
wspd is highly correlated with wpgtHigh correlation
wpgt is highly correlated with wspdHigh correlation
PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] is highly correlated with tmax and 1 other fieldsHigh correlation
tavg is highly correlated with tmin and 3 other fieldsHigh correlation
tmin is highly correlated with tavg and 3 other fieldsHigh correlation
tmax is highly correlated with PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] and 4 other fieldsHigh correlation
snow is highly correlated with tavg and 2 other fieldsHigh correlation
wdir is highly correlated with wspdHigh correlation
wspd is highly correlated with wdir and 1 other fieldsHigh correlation
wpgt is highly correlated with wspdHigh correlation
pres is highly correlated with tminHigh correlation
tsun is highly correlated with PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] and 2 other fieldsHigh correlation
PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh] has 20 (1.4%) missing values Missing
time has unique values Unique
prcp has 835 (57.2%) zeros Zeros
snow has 1388 (95.0%) zeros Zeros
tsun has 197 (13.5%) zeros Zeros

Reproduction

Analysis started2022-02-16 20:02:28.108102
Analysis finished2022-02-16 20:02:49.491144
Duration21.38 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

time
Date

UNIQUE

Distinct1461
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Minimum2018-01-01 00:00:00
Maximum2021-12-31 00:00:00
2022-02-16T21:02:49.585145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:49.760145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1437
Distinct (%)99.7%
Missing20
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean240.8420305
Minimum0
Maximum589.171
Zeros4
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:49.996145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.318
Q192.467
median225.034
Q3373.713
95-th percentile512.369
Maximum589.171
Range589.171
Interquartile range (IQR)281.246

Descriptive statistics

Standard deviation160.158681
Coefficient of variation (CV)0.6649947296
Kurtosis-1.154610092
Mean240.8420305
Median Absolute Deviation (MAD)141.304
Skewness0.2461214881
Sum347053.366
Variance25650.80309
MonotonicityNot monotonic
2022-02-16T21:02:50.164145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
0.3%
173.792
 
0.1%
117.9281
 
0.1%
439.6721
 
0.1%
376.911
 
0.1%
118.4171
 
0.1%
208.4011
 
0.1%
296.3761
 
0.1%
354.5051
 
0.1%
149.2791
 
0.1%
Other values (1427)1427
97.7%
(Missing)20
 
1.4%
ValueCountFrequency (%)
04
0.3%
0.0041
 
0.1%
0.1071
 
0.1%
0.1541
 
0.1%
0.4541
 
0.1%
0.6491
 
0.1%
0.7021
 
0.1%
1.5551
 
0.1%
1.8931
 
0.1%
2.181
 
0.1%
ValueCountFrequency (%)
589.1711
0.1%
581.5041
0.1%
577.7961
0.1%
574.4091
0.1%
572.7621
0.1%
572.3861
0.1%
567.2571
0.1%
558.5611
0.1%
557.9541
0.1%
555.4011
0.1%

tavg
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct305
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.964887064
Minimum-14.1
Maximum28.7
Zeros4
Zeros (%)0.3%
Negative113
Negative (%)7.7%
Memory size11.5 KiB
2022-02-16T21:02:50.339145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-14.1
5-th percentile-1.1
Q14.1
median9.8
Q316.2
95-th percentile21.7
Maximum28.7
Range42.8
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation7.514132288
Coefficient of variation (CV)0.7540609583
Kurtosis-0.6376825513
Mean9.964887064
Median Absolute Deviation (MAD)6.1
Skewness-0.05126476751
Sum14558.7
Variance56.46218405
MonotonicityNot monotonic
2022-02-16T21:02:50.505145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.114
 
1.0%
17.513
 
0.9%
4.213
 
0.9%
14.712
 
0.8%
9.812
 
0.8%
18.812
 
0.8%
17.411
 
0.8%
5.511
 
0.8%
611
 
0.8%
1.511
 
0.8%
Other values (295)1341
91.8%
ValueCountFrequency (%)
-14.11
0.1%
-12.51
0.1%
-112
0.1%
-10.62
0.1%
-101
0.1%
-9.91
0.1%
-8.82
0.1%
-8.51
0.1%
-8.21
0.1%
-81
0.1%
ValueCountFrequency (%)
28.71
 
0.1%
282
0.1%
27.71
 
0.1%
27.51
 
0.1%
27.21
 
0.1%
26.82
0.1%
26.41
 
0.1%
26.13
0.2%
262
0.1%
25.91
 
0.1%

tmin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct285
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.491581109
Minimum-19.1
Maximum20.4
Zeros8
Zeros (%)0.5%
Negative323
Negative (%)22.1%
Memory size11.5 KiB
2022-02-16T21:02:50.672145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-19.1
5-th percentile-4.6
Q10.3
median5.2
Q310.8
95-th percentile15.7
Maximum20.4
Range39.5
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.575663228
Coefficient of variation (CV)1.197408014
Kurtosis-0.3488156349
Mean5.491581109
Median Absolute Deviation (MAD)5.2
Skewness-0.1779506337
Sum8023.2
Variance43.23934688
MonotonicityNot monotonic
2022-02-16T21:02:50.847145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.215
 
1.0%
1.715
 
1.0%
0.115
 
1.0%
3.714
 
1.0%
-0.214
 
1.0%
1.214
 
1.0%
813
 
0.9%
10.313
 
0.9%
-0.713
 
0.9%
7.312
 
0.8%
Other values (275)1323
90.6%
ValueCountFrequency (%)
-19.11
0.1%
-17.51
0.1%
-16.71
0.1%
-14.71
0.1%
-14.61
0.1%
-14.11
0.1%
-12.91
0.1%
-12.81
0.1%
-12.71
0.1%
-12.61
0.1%
ValueCountFrequency (%)
20.41
 
0.1%
20.21
 
0.1%
19.82
0.1%
19.71
 
0.1%
19.52
0.1%
19.41
 
0.1%
18.62
0.1%
18.44
0.3%
18.21
 
0.1%
18.14
0.3%

tmax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct369
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.24531143
Minimum-11.2
Maximum36.5
Zeros3
Zeros (%)0.2%
Negative48
Negative (%)3.3%
Memory size11.5 KiB
2022-02-16T21:02:51.021145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-11.2
5-th percentile1
Q17.1
median14.3
Q321.3
95-th percentile28.5
Maximum36.5
Range47.7
Interquartile range (IQR)14.2

Descriptive statistics

Standard deviation8.805830898
Coefficient of variation (CV)0.6181564328
Kurtosis-0.7668921009
Mean14.24531143
Median Absolute Deviation (MAD)7.1
Skewness0.03949451129
Sum20812.4
Variance77.5426578
MonotonicityNot monotonic
2022-02-16T21:02:51.190145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.512
 
0.8%
21.212
 
0.8%
14.511
 
0.8%
12.710
 
0.7%
8.610
 
0.7%
23.910
 
0.7%
7.89
 
0.6%
6.49
 
0.6%
10.29
 
0.6%
4.39
 
0.6%
Other values (359)1360
93.1%
ValueCountFrequency (%)
-11.21
0.1%
-10.41
0.1%
-8.21
0.1%
-8.11
0.1%
-7.71
0.1%
-7.11
0.1%
-6.11
0.1%
-5.91
0.1%
-5.51
0.1%
-5.31
0.1%
ValueCountFrequency (%)
36.51
0.1%
36.11
0.1%
35.91
0.1%
35.11
0.1%
34.81
0.1%
34.71
0.1%
34.31
0.1%
34.21
0.1%
33.92
0.1%
33.32
0.1%

prcp
Real number (ℝ≥0)

ZEROS

Distinct130
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.458932238
Minimum0
Maximum41.4
Zeros835
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:51.358145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.4
Maximum41.4
Range41.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.893307916
Coefficient of variation (CV)2.668600922
Kurtosis33.36435364
Mean1.458932238
Median Absolute Deviation (MAD)0
Skewness5.069785341
Sum2131.5
Variance15.15784653
MonotonicityNot monotonic
2022-02-16T21:02:51.518145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0835
57.2%
0.164
 
4.4%
0.243
 
2.9%
0.333
 
2.3%
0.724
 
1.6%
0.422
 
1.5%
0.520
 
1.4%
0.619
 
1.3%
115
 
1.0%
1.115
 
1.0%
Other values (120)371
25.4%
ValueCountFrequency (%)
0835
57.2%
0.164
 
4.4%
0.243
 
2.9%
0.333
 
2.3%
0.422
 
1.5%
0.520
 
1.4%
0.619
 
1.3%
0.724
 
1.6%
0.813
 
0.9%
0.910
 
0.7%
ValueCountFrequency (%)
41.41
0.1%
371
0.1%
34.61
0.1%
34.21
0.1%
33.91
0.1%
32.51
0.1%
311
0.1%
28.31
0.1%
22.81
0.1%
221
0.1%

snow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.449691992
Minimum0
Maximum320
Zeros1388
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:51.656145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum320
Range320
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.65117675
Coefficient of variation (CV)7.145906595
Kurtosis111.7372834
Mean3.449691992
Median Absolute Deviation (MAD)0
Skewness10.1417979
Sum5040
Variance607.6805153
MonotonicityNot monotonic
2022-02-16T21:02:51.838145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
01388
95.0%
2017
 
1.2%
1014
 
1.0%
408
 
0.5%
306
 
0.4%
705
 
0.3%
605
 
0.3%
504
 
0.3%
2803
 
0.2%
3002
 
0.1%
Other values (8)9
 
0.6%
ValueCountFrequency (%)
01388
95.0%
1014
 
1.0%
2017
 
1.2%
306
 
0.4%
408
 
0.5%
504
 
0.3%
605
 
0.3%
705
 
0.3%
801
 
0.1%
1201
 
0.1%
ValueCountFrequency (%)
3202
0.1%
3002
0.1%
2803
0.2%
2601
 
0.1%
2201
 
0.1%
1801
 
0.1%
1401
 
0.1%
1301
 
0.1%
1201
 
0.1%
801
 
0.1%

wdir
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1091
Distinct (%)74.8%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean200.7276406
Minimum0.6
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:51.991146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile34.97
Q1154.075
median223.5
Q3257.525
95-th percentile322.115
Maximum360
Range359.4
Interquartile range (IQR)103.45

Descriptive statistics

Standard deviation88.49227303
Coefficient of variation (CV)0.4408574363
Kurtosis-0.521796702
Mean200.7276406
Median Absolute Deviation (MAD)41.15
Skewness-0.6634377238
Sum292660.9
Variance7830.882386
MonotonicityNot monotonic
2022-02-16T21:02:52.160145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247.66
 
0.4%
216.35
 
0.3%
244.74
 
0.3%
1954
 
0.3%
208.74
 
0.3%
45.54
 
0.3%
231.64
 
0.3%
213.34
 
0.3%
240.34
 
0.3%
225.84
 
0.3%
Other values (1081)1415
96.9%
ValueCountFrequency (%)
0.61
0.1%
2.11
0.1%
2.81
0.1%
31
0.1%
3.51
0.1%
3.71
0.1%
4.61
0.1%
4.82
0.1%
7.31
0.1%
7.61
0.1%
ValueCountFrequency (%)
3601
0.1%
359.91
0.1%
359.61
0.1%
359.11
0.1%
357.61
0.1%
357.31
0.1%
356.91
0.1%
356.81
0.1%
355.41
0.1%
355.21
0.1%

wspd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct235
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.52758385
Minimum3.2
Maximum47.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:52.338146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile6.3
Q19.4
median13
Q317.6
95-th percentile28.8
Maximum47.9
Range44.7
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation7.124320605
Coefficient of variation (CV)0.4903995517
Kurtosis1.933839708
Mean14.52758385
Median Absolute Deviation (MAD)4
Skewness1.290294017
Sum21224.8
Variance50.75594409
MonotonicityNot monotonic
2022-02-16T21:02:52.502145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.441
 
2.8%
9.440
 
2.7%
7.936
 
2.5%
11.233
 
2.3%
10.133
 
2.3%
8.633
 
2.3%
11.532
 
2.2%
931
 
2.1%
9.730
 
2.1%
1330
 
2.1%
Other values (225)1122
76.8%
ValueCountFrequency (%)
3.22
0.1%
3.51
 
0.1%
3.91
 
0.1%
41
 
0.1%
4.11
 
0.1%
4.23
0.2%
4.33
0.2%
4.41
 
0.1%
4.62
0.1%
4.73
0.2%
ValueCountFrequency (%)
47.91
 
0.1%
46.41
 
0.1%
45.41
 
0.1%
42.51
 
0.1%
42.12
0.1%
41.43
0.2%
411
 
0.1%
40.31
 
0.1%
38.93
0.2%
38.51
 
0.1%

wpgt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)13.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean38.45592872
Minimum10.1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:52.660145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile18.7
Q127.2
median36
Q346.6
95-th percentile68.04
Maximum130
Range119.9
Interquartile range (IQR)19.4

Descriptive statistics

Standard deviation15.40470098
Coefficient of variation (CV)0.4005806514
Kurtosis2.072703334
Mean38.45592872
Median Absolute Deviation (MAD)9.4
Skewness1.127771134
Sum56107.2
Variance237.3048122
MonotonicityNot monotonic
2022-02-16T21:02:52.829144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.824
 
1.6%
25.224
 
1.6%
27.723
 
1.6%
31.719
 
1.3%
43.619
 
1.3%
2319
 
1.3%
27.418
 
1.2%
2718
 
1.2%
37.418
 
1.2%
28.418
 
1.2%
Other values (191)1259
86.2%
ValueCountFrequency (%)
10.11
 
0.1%
11.23
0.2%
11.91
 
0.1%
12.21
 
0.1%
12.61
 
0.1%
13.31
 
0.1%
142
 
0.1%
14.43
0.2%
15.14
0.3%
15.55
0.3%
ValueCountFrequency (%)
1301
0.1%
109.11
0.1%
106.61
0.1%
991
0.1%
98.61
0.1%
97.91
0.1%
97.21
0.1%
96.51
0.1%
95.41
0.1%
92.91
0.1%

pres
Real number (ℝ≥0)

HIGH CORRELATION

Distinct387
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1015.804517
Minimum981.6
Maximum1046.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:52.998145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum981.6
5-th percentile1000.2
Q11010.5
median1016
Q31021.6
95-th percentile1029.9
Maximum1046.1
Range64.5
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation9.051745846
Coefficient of variation (CV)0.008910913164
Kurtosis0.3860187809
Mean1015.804517
Median Absolute Deviation (MAD)5.6
Skewness-0.1901106498
Sum1484090.4
Variance81.93410287
MonotonicityNot monotonic
2022-02-16T21:02:53.160145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101715
 
1.0%
101413
 
0.9%
1019.712
 
0.8%
1010.612
 
0.8%
1013.312
 
0.8%
1012.711
 
0.8%
1015.611
 
0.8%
1016.811
 
0.8%
1016.111
 
0.8%
1016.911
 
0.8%
Other values (377)1342
91.9%
ValueCountFrequency (%)
981.61
0.1%
9831
0.1%
9881
0.1%
988.71
0.1%
988.81
0.1%
989.81
0.1%
990.61
0.1%
990.91
0.1%
991.81
0.1%
9922
0.1%
ValueCountFrequency (%)
1046.11
0.1%
10421
0.1%
1041.82
0.1%
1041.51
0.1%
1041.21
0.1%
1039.42
0.1%
1039.11
0.1%
1038.51
0.1%
1038.11
0.1%
1037.92
0.1%

tsun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct636
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.7364819
Minimum0
Maximum909
Zeros197
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-02-16T21:02:53.334145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144
median244
Q3505
95-th percentile785
Maximum909
Range909
Interquartile range (IQR)461

Descriptive statistics

Standard deviation265.8029391
Coefficient of variation (CV)0.8897572115
Kurtosis-0.9471186744
Mean298.7364819
Median Absolute Deviation (MAD)218
Skewness0.5300755639
Sum436454
Variance70651.20243
MonotonicityNot monotonic
2022-02-16T21:02:53.494145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0197
 
13.5%
113
 
0.9%
211
 
0.8%
38
 
0.5%
77
 
0.5%
1077
 
0.5%
146
 
0.4%
126
 
0.4%
4456
 
0.4%
1796
 
0.4%
Other values (626)1194
81.7%
ValueCountFrequency (%)
0197
13.5%
113
 
0.9%
211
 
0.8%
38
 
0.5%
45
 
0.3%
55
 
0.3%
65
 
0.3%
77
 
0.5%
85
 
0.3%
106
 
0.4%
ValueCountFrequency (%)
9091
0.1%
9071
0.1%
9061
0.1%
9051
0.1%
9041
0.1%
9021
0.1%
8991
0.1%
8981
0.1%
8961
0.1%
8941
0.1%

Interactions

2022-02-16T21:02:47.266145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:31.452618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:33.276137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:34.813145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:36.399145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:37.883145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:39.332145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:40.865146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:42.656145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:44.176147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:45.730145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:47.400145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:31.615618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:33.419136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:34.963145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:36.598145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:38.014144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:39.468145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:41.010145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:42.792145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:44.397145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:45.874146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:47.518145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:33.557137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:42.918145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:44.524145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:39.733145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:41.320144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:33.838137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:35.407145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:38.457145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:46.468145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:48.348145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:32.785137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:34.375136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:38.953145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:40.462145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:42.104147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:43.775145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:45.332144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:46.877145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:48.477146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:32.950137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:34.500137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T21:02:37.629145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:39.079145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:40.596145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:42.338151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:43.907146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:45.462145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:47.008145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:48.611145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:33.140136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:34.693136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:36.263146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:37.761145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:39.211145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:40.736145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:42.514145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:44.042145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:45.601145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T21:02:47.142144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-16T21:02:53.701144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-16T21:02:53.896145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-16T21:02:54.089145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-16T21:02:54.280145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-16T21:02:48.830144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-16T21:02:49.113145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-16T21:02:49.296145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-16T21:02:49.394145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

timePVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh]tavgtmintmaxprcpsnowwdirwspdwpgtprestsun
02018-01-01117.9286.73.710.70.10.0213.432.466.61001.6183.0
12018-02-0169.2722.3-0.74.71.10.0229.321.248.21001.1229.0
22018-03-0156.669-8.8-12.8-4.30.00.066.719.143.91014.3575.0
32018-04-0130.2603.10.66.12.50.0284.417.338.21006.053.0
42018-05-0124.5579.34.113.40.00.0230.427.461.91010.7676.0
52018-06-0144.13520.315.824.30.00.0220.915.839.61014.5522.0
62018-07-017.20616.110.021.30.00.068.113.041.01018.7907.0
72018-08-0110.62324.419.431.14.20.03.010.127.71018.5470.0
82018-09-0125.98514.25.420.50.00.0355.410.135.31024.1578.0
92018-10-0174.8456.92.513.71.20.0269.817.651.81015.0124.0

Last rows

timePVA Wandersleben / Gesamtertrag / Zaehleraenderung [kWh]tavgtmintmaxprcpsnowwdirwspdwpgtprestsun
14512021-12-2252.923-4.2-8.10.40.00.0180.64.915.51027.0191.0
14522021-12-2329.9260.1-4.04.70.60.0215.116.349.71013.80.0
14532021-12-2435.9464.60.15.53.10.0249.225.163.41003.93.0
14542021-12-250.454-4.0-7.6-0.16.520.066.512.731.01011.50.0
14552021-12-260.702-3.6-7.70.50.020.073.66.916.21011.40.0
14562021-12-276.1902.2-0.24.52.820.0165.210.530.21004.7135.0
14572021-12-2859.9795.03.76.70.40.0200.418.448.2998.010.0
14582021-12-2926.3745.83.57.31.60.0243.915.238.51005.40.0
14592021-12-3032.64910.77.813.30.10.0244.623.252.91014.810.0
14602021-12-31119.15010.79.811.70.00.0237.822.568.01019.3208.0